The Composite Strain Index (COSI) quantifies biomechanical stressors for complex tasks consisting of exertions at different force levels and/or with different exertion times. The Cumulative Strain Index (CUSI) further integrates biomechanical stressors from different tasks to quantify exposure for the entire work shift. The paper provides methodologies to compute COSI and CUSI along with examples. Complex task simulation produced 169,214 distinct tasks. Use of average, time-weighted average (TWA) and peak force and COSI classified 66.9, 28.2, 100 and 38.9% of tasks as hazardous, respectively. For job rotation the simulation produced 10,920 distinct jobs. TWA COSI, peak task COSI and CUSI classified 36.5, 78.1 and 66.6% jobs as hazardous, respectively. The results suggest that the TWA approach systematically underestimates the biomechanical stressors and peak approach overestimates biomechanical stressors, both at the task and job level. It is believed that the COSI and CUSI partially address these underestimations and overestimations of biomechanical stressors.Practitioner Summary: COSI quantifies exposure when applied hand force and/or duration of that force changes during a task cycle. CUSI integrates physical exposures from job rotation. These should be valuable tools for designing and analysing tasks and job rotation to determine risk of musculoskeletal injuries.

The Revised Strain Index (RSI) is a distal upper extremity (DUE) physical exposure assessment model based on: intensity of exertion, frequency of exertion, duration per exertion, hand/wrist posture and duration of task per day. The RSI improves upon the 1995 Strain Index (SI) by using continuous rather than categorical multipliers, and replacing duty cycle with duration per exertion. In a simulation of 13,944 tasks, the RSI and 1995 SI showed good agreement in risk predictions for 1995 SI scores3 (safe) and>13.5 (hazardous). For tasks with 1995 SI scores of>3 and13.5, the two models showed marked disagreement, with the RSI providing much greater discriminations between safe' and hazardous' tasks for various combinations of force, repetition and duty cycle. We believe the RSI is a substantially improved model that will be useful for DUE task analysis, intervention and design.Practitioner Summary: RSI is a substantial improvement over the 1995 SI. It should be a valuable tool for designing and analysing tasks to determine risk of musculoskeletal injuries. RSI is applicable to a wide variety of tasks including very low force and very high repetition tasks such as keyboard use.

OBJECTIVE:The aim of this study was to evaluate relationships between the revised NIOSH lifting equation (RNLE) and risk of low-back pain (LBP).BACKGROUND:The RNLE is commonly used to quantify job physical stressors to the low back from lifting and/or lowering of loads. There is no prospective study on the relationship between RNLE and LBP that includes accounting for relevant covariates.METHOD:A cohort of 258 incident-eligible workers from 30 diverse facilities was followed for up to 4.5 years. Job physical exposures were individually measured. Worker demographics, medical history, psychosocial factors, hobbies, and current LBP were obtained at baseline. The cohort was followed monthly to ascertain development of LBP and quarterly to determine changes in job physical exposure. The relationship between LBP and peak lifting index (PLI) and peak composite lifting index (PCLI) were tested in multivariate models using proportional hazards regression.RESULTS:Point and lifetime prevalences of LBP at baseline were 7.1% and 75.1%, respectively. During follow-up, there were 123 incident LBP cases. Factors predicting development of LBP included job physical exposure (PLI and PCLI), history of LBP, psychosocial factors, and housework. In adjusted models, risk (hazard ratio [HR]) increased per-unit increase in PLI and PCLI (p = .05 and .02; maximum HR = 4.3 and 4.2, respectively). PLI suggested a continuous increase in risk with an increase in PLI, whereas the PCLI showed elevated, but somewhat reduced, risk at higher exposures.CONCLUSION:Job physical stressors are associated with increased risk of LBP. Data suggest that the PLI and PCLI are useful metrics for estimating exposure to job physical stressors.

Objective: To evaluate relationships between lifting and lowering of loads and risk of low back pain resulting in medication use (M-LBP). Methods: At baseline, worker demographics, psychosocial factors, hobbies, LBP history, and lifting and lowering (quantified using the Revised NIOSH Lifting Equation) were assessed. A cohort of 258 incident-eligible workers was followed up for 4.5 years to determine new M-LBP cases and changes in lifting/lowering requirements. Proportional hazards regression with time-varying covariates was used to model associations. Results: Factors predicting M-LBP included peak lifting index (PLI) and composite lifting index (PCLI), LBP history, anxiety, and housework. In adjusted models, PLI and PCLI showed exposure-response relationships with peak hazard ratios of 3.8 and 4.3, respectively (P <= 0.02). Conclusions: Lifting of loads is associated with increased risk of M-LBP. The PLI and PCLI are useful metrics for estimating the risk of M-LBP from lifting.

Objective: This manuscript systematically quantifies multiple measures of low-back pain (LBP) prevalence by pain rating in a large, multisite cohort of workers. Background: Published LBP prevalence rates vary. Studies rely on one measure of LBP and none report prevalence stratified by pain rating. Method: Cross-sectional analyses of baseline data from a multicenter prospective cohort study were performed to evaluate differences in lifetime prevalence, 1-month period prevalence, and point prevalence of LBP. Workers were from 28 different employment settings in 4 diverse U.S. states. All workers completed computerized questionnaires and structured interviews. LBP prevalence measures were stratified by pain ratings. Results: A total of 828 subjects had complete health data at baseline. Lifetime prevalence, 1-month period prevalence, and point prevalence for any LBP (≥1/10) were 63.4%, 44.0%, and 20.8% respectively. Prevalence of LBP decreased with increasing pain ratings. As an example, using a threshold of LBP ≥ 3/10 pain, prevalence measures were 61.0%, 37.6%, and 16.7% respectively. A threshold of LBP ≥ 5/10 had prevalence measures of 51.2%, 22.9%, and 9.9% respectively. Age, systolic and diastolic blood pressure, high cholesterol, high blood pressure, and tobacco use were statistically significantly related to lifetime prevalence of LBP. Conclusion: Lifetime LBP prevalence, 1-month period prevalence, and point prevalence stratified by pain ratings demonstrate a wide variation of prevalence measures of LBP and self-reported pain ratings. Higher pain rating thresholds yield lower prevalence measures and may impact assessments of risk factors. Differences in pain ratings may allow for focused surveillance within an occupational cohort.

To evaluate relationships between lifting and lowering of loads and risk of low back pain resulting in medication use (M-LBP).At baseline, worker demographics, psychosocial factors, hobbies, LBP history, and lifting and lowering (quantified using the Revised NIOSH Lifting Equation) were assessed. A cohort of 258 incident-eligible workers was followed up for 4.5 years to determine new M-LBP cases and changes in lifting/lowering requirements. Proportional hazards regression with time-varying covariates was used to model associations.Factors predicting M-LBP included peak lifting index (PLI) and composite lifting index (PCLI), LBP history, anxiety, and housework. In adjusted models, PLI and PCLI showed exposure-response relationships with peak hazard ratios of 3.8 and 4.3, respectively (P ≤ 0.02).Lifting of loads is associated with increased risk of M-LBP. The PLI and PCLI are useful metrics for estimating the risk of M-LBP from lifting.

Objective: The aim of this study was to investigate the relationship between the revised NIOSH lifting equation (RNLE) and risk of seeking care for low-back pain (SC-LBP). Background: The RNLE is commonly used to quantify low-back physical stressors from lifting/lowering of loads in workplaces. There is no prospective study on relationship between RNLE and SC-LBP. Method: A cohort of 258 incident-eligible workers from 30 diverse facilities was followed for up to 4.5 years. Job physical exposures were individually measured. Worker demographics, medical history, psychosocial factors, hobbies, and current low-back pain were obtained at baseline. The cohort was followed monthly to ascertain SC-LBP and quarterly to determine changes in physical exposure. Associations between SC-LBP and both the peak lifting index (PLI) and peak composite lifting index (PCLI) were tested in multivariate models using proportional hazards regression. Results: SC-LBP lifetime prevalence at baseline was 31.9%, and there were 24 incident cases during follow-up. Factors predicting SC-LBP included job physical exposure (PLI and PCLI), history of low-back pain, age, female gender, and lower body mass index. In adjusted models, risk (hazard ratio [HR]) increased per unit increase in PLI and PCLI ( p = .03 and .02, and maximum HR = 23.0 and 21.9, respectively). Whereas PCLI suggested a continuous increase in risk with an increase in PCLI, the PLI showed elevated, though somewhat reduced, risk at higher exposures (HR = 14.9 at PLI = 6). Conclusion: Job physical stressors are associated with increased risk of SC-LBP. Data suggest that both the PLI and PCLI are useful metrics for estimating exposure to job physical stressors.

BACKGROUND:Low Back Pain (LBP) is a common and costly problem, with variation in prevalence. Epidemiological reports of rating of pain intensity and location within the low back area are rare. The objective is to describe LBP in a large, multi-center, occupational cohort detailing both point and 1-month period prevalence of LBP by location and intensity measures at baseline.METHODS:In this cross-sectional report from a prospective cohort study, 828 participants were workers enrolled from 30 facilities performing a variety of manual material handling tasks. All participants underwent a structured interview detailing pain rating and location. Symptoms in the lower extremities, demographic and other data were collected. Body mass indices were measured. Outcomes are pain rating (0-10) in five defined lumbar back areas (i) LBP in the past month and (ii) LBP on the day of enrollment. Pain ratings were reported on a 0-10 scale and subsequently collapsed with ratings of 1-3, 4-6 and 7-10 classified as low, medium and high respectively.RESULTS:172 (20.8%) and 364 (44.0%) of the 828 participants reported pain on the day of enrollment or within the past month, respectively. The most common area of LBP was in the immediate paraspinal area with 130 (75.6%) participants with point prevalence LBP and 278 (77.4%) with 1-month period prevalence reported having LBP in the immediate paraspinal area. Among those 364 reporting 1-month period prevalence pain, ratings varied widely with 116 (31.9%) reporting ratings classified as low, 170 (46.7%) medium and 78 (21.4%) providing high pain ratings in any location. Among the 278 reporting 1-month period prevalence pain in the immediate paraspinal area, 89 (32.0%) reported ratings classified as low, 129 (46.4%), medium and 60 (21.6%) high pain ratings.CONCLUSIONS:Pain ratings varied widely, however less variability was seen in pain location, with immediate paraspinal region being the most common. Variations may suggest different etiological factors related to LBP. Aggregation of different locations of pain or different intensities of pain into one binary classification of LBP may result in loss of information which may potentially be useful in prevention or treatment of LBP.

Few prospective cohort studies of workplace low back pain (LBP) with quantified job physical exposure have been performed. There are few prospective epidemiological studies for LBP occupational risk factors and reported data generally have few adjustments for many personal and psychosocial factors.

Fifteen raters individually, and in five teams of three, evaluated the test-retest repeatability of published data collection and rating methods of the Strain Index by analyzing 61 job video files twice over a 5-month period. Raters estimated average and peak hand forces, measured Duration of Exertion, cycle time, and exertions per job cycle, calculated percent Duration of Exertion and Efforts per Minute, and assigned ratings for five of the six Strain Index task variables. Twelve additional jobs were analyzed to determine Strain Index Score and hazard classification. Intraclass correlation (ICC) coefficients for task variable ratings and accompanying data ranged from 0.66 to 0.95 for both individuals and teams. The Strain Index Score ICC(2,1) for individuals and teams were 0.56 and 0.82, respectively. Intra-rater reliability for the hazard classification was 0.81 for individuals and 0.88 for teams. The results indicate that the Strain Index has good test-retest reliability.

Department of Environmental & Occupational Health School of Rural Public Health, Texas A&M University System, Health Science Center, College Station, TX, Email: [email protected]

An investigation of postural and chair design impacts upon seat pan interface pressure has been performed in an effort to identify whether differences in posture or chair design result in greater pressure differences. Investigation of postural variables focused on trunk–thigh angle and use of armrests. Twelve ergonomic office chairs were used to assess chair design differences. Both male and female subjects were included. Gender effects were controlled through use of a repeated Latin square design, with squares defined by gender. Significant gender-based interaction was observed amongst postural treatments and chair effects. Postural treatments, chairs designs, and participant effects all resulted in significant interface pressure differences, though gender-based interaction yielded some non-additivity of results between males and females. The final conclusion drawn from the results is that chair design differences had the greatest effect on seat pan interface pressure, followed by participant effects, and lastly postural treatments.

The Strain Index is one of several tools available to evaluate exposure to musculoskeletal stressors in the workplace in order to predict whether workers are at an increased risk of developing distal upper extremity disorders. The purpose of this study was to determine the inter-rater reliability of the Strain Index. Fifteen raters initially estimated or measured data for five Strain Index task variables: (1) intensity of exertion, (2) duration of exertion, (3) efforts per minute, (4) hand/wrist posture, and (5) speed of work, using a computer to view 61 video segments of single task jobs. Video segments were organized to provide a balanced number for each of the five variables under study, where each segment was used to provide data for a single variable. Raters then assigned rating values to each task according to published procedures. For an additional 12 segments, raters performed complete Strain Index analyses, including calculation of Strain Index scores, which were used to determine hazard classifications. Raters assessed the tasks both as individuals and as members of five three-person teams. Inter-rater reliabilities of the procedures leading to and including the Strain Index scores were assessed using an intraclass correlation coefficient, (ICC(2,1)). Inter-rater reliability for the dichotomous hazard classification was assessed using Kuder-Richardson-20 (KR-20, an ICC for dichotomous data). For task variables and estimated data, ICC(2,1) varied between 0.66–0.84 for individuals and 0.48–0.93 for teams. The Strain Index score had an ICC(2,1) of 0.43 and 0.64 for individuals and teams, respectively. For the most important variable, hazard classification, KR-20 was 0.91 for the individuals and 0.89 for the teams.